Applications of high-throughput sequencing (such as "ChIP-seq" and "RNA-seq") have recently transformed our ability to study protein-DNA interactions, chromatin modifications, and gene expression through their precise quantification of these phenomena genome-wide. Although numerous groundbreaking discoveries have emerged from the application of these techniques, few efforts have considered that the genome is in fact represented by two copies that often differ in functionally important ways. Heterozygous polymorphisms (such as SNPs) in a biological sample that is subject to ChIP-seq or RNA-seq provide a means to quantify allele-specific events by discriminating sequence reads that map to one allele versus the other, since biased representation of alleles among sequence reads can reveal sites/genes that are affected by cis- acting polymorphisms. It is our objective to create tools that will allow the community to more easily analyze their data for allele-specific signals;apply these tools to dozens of existing data sets;and generate new ChIP-seq and RNA-seq data that are ideally suited for the comprehensive characterization of allele-specific biases. PUBLIC HEALTH RELEVANCE: Elucidating the functional consequences of genetic polymorphisms is crucial for understanding the genetic risk factors underlying human diseases. This project will advance our knowledge of how cis-acting polymorphisms, which are a widespread source of phenotypic diversity, affect mRNA abundances. In addition, application of our methodology in human samples has the potential to uncover the precise causal polymorphisms underlying significant associations revealed by genome-wide disease association studies, which is important e.g. for performing targeted follow-up studies and functional assays to better understand each polymorphism and its effects on both gene expression and disease.